Keeping 123 the spacecraft (S/C) healthy and productive is the responsibility and the main concern of the S/C flight control team (FCT). Space weather includes effects and conditions that favour the aging of a S/C and its instruments, e.g. degradation of sensors and solar arrays by charged particles and single event upsets (SEU). It is worth to stress out that it is hard to detect when the environmental conditions of a S/C are safe and when they are hazardous. So far, the most widely used approach of a FCT to counteract these effects has been to play safe. Playing safe, i.e. invoking counter measures early enough and keeping them for a long enough period, is a means to reduce the risk, but it is not the most efficient one. The dynamics of space weather could lead to situations where the instruments are shielded hours before the conditions become really hazardous. On the other hand it might happen that the FCT assumes safe conditions long before they actually are. single source, facilitates the analysis of this data therefore enhancing user awareness of space weather effects and possible cause-effect relationships between space weather events and S/C anomalous conditions.The system provides an interface to external applications to include data produced by physical models for the space environment and its effects. Furthermore, trained artificial neural networks or other plug-in tools can access all the data and produce forecasts for certain parameters. These forecasts can be fed back into the data-warehouse and be used for monitoring, analysis and operations planning.The reference mission for SEIS is INTEGRAL, ESA's gamma ray observatory. It also includes data from other ESA missions: ENVISAT and XMM. The main objective for INTEGRAL is to maximize the payload instruments' time while operating in safe condition, resulting in an increase of the S/C's productivity.The system is a prototype whose objective is the proof of concept for new emerging technologies applicable to the mission operations domain, increasing the quality and effectiveness of mission operations management.The paper presents the system, focusing on the application of the new technologies to support the FCT in their critical decision making process and its expected impact in the INTEGRAL's operations strategy.

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Abstract. The radiation environment encountered in space by satellites is extremely variable and depends mainly on the satellite position and space weather. Although models for the concerned processes are available, most of them only represent the average conditions and neglect the dynamics of the system. Accurate prediction of the radiation environment remains an unsolved problem. Space weather can cause manifold problems to spacecraft (S/C) components, such as degradation of sensors and solar arrays and changes in on-board memories by Single Event Upsets (SEU). The final effect is a degradation of the S/C overall performance and in extreme cases complete unavailability of services. When certain alarm conditions are reached, risk avoidance procedures may be invoked, e.g. switching off high voltages/biases/filters etc. and transition to protected operating modes. Once the detectors are off, there is just a rough estimation of when the conditions are safe again. Better prediction of radiation conditions and more accurate information could greatly improve these operations. Therefore it is necessary to monitor and predict the space weather effects and improve the space weather services.In this paper we present a system for correlation, monitoring and predicting of space weather data, which is being developed by UNINOVA (Institute for the Development of New Technologies, Caparica, Portugal) in the frame of the European Space Agency (ESA) project, Space Environment Information System for Mission Control Purposes (SEIS) project.This decision support system (DSS) provides Flight Control Teams with useful Space Weather information (past, current and future) to increase the ability to protect S/C components from hazardous events and therefore prolong the lifetime of satellites and their scientific return. The system design was inspired by traditional business oriented DSS, based on Data Warehousing storage techniques, which collect and integrate historical and real-time data from heterogeneous sources, e.g. Space Weather data providers, S/C Telemetry and Orbital data and historical SEU records. The services provided include exploration and correlation analysis of data by established OLAP (On Line Analytical Processing) techniques, and near real-time monitoring of S/C instruments susceptible to Space Weather conditions. The monitoring service will directly depend on a Knowledge Base (representation of the domain experts knowledge), allowing it to suggest appropriate recovery actions whenever possible S/C damaging conditions are identified. Additionally, the system will incorporate forecasting services of well-known and commonly used physical radiation models, as well as new prediction techniques based on nonlinear models such as Artificial Neural Networks. For more information, please refer to:

Space Weather is the combination of conditions on the sun, in the solar wind, magnetosphere, ionosphere and thermosphere that can influence the performance and reliability of space-borne and groundbased technological systems and can endanger human life or health. Space Weather can cause manifold problems to spacecraft (S/C) components, such as degradation of sensors and solar arrays and changes in on-board memories by Single Event Upsets (SEU). The final effect is the degradation of the S/C's overall performance and in extreme cases complete unavailability of services. When certain alarm conditions are reached, risk avoidance procedures may be invoked, e.g. switching off high voltages/biases/filters etc. and transition to protected operating modes. In this paper a Decision Support System (DDS) is outlined that provides Flight Control Teams with useful space weather information (past, current and predicted) to increase the ability to protect spacecraft components from hazardous events and therefore prolong the lifetime of the satellites. The system design was inspired by traditional business oriented DDS, based on Data Warehousing storage techniques, which collect and integrate historical and real-time data from heterogeneous sources. The provided services include exploration and correlation analysis of data by established On Line Analytical Processing (OLAP) techniques and near real-time monitoring of spacecraft sensors susceptible to space weather conditions. The forecasting services are based on well-known and commonly used physical radiation models complemented by prediction techniques based on non-linear models such as Artificial Neural Networks. The system main goal is to support the spacecraft operators in taking decisions about how to react to space weather conditions possibly causing spacecraft degradations. In addition the system will facilitate the awareness and understanding of how Space Weather affects satellite performance, paving the way to possibly prolong mission lifetime and increase the quality of services and the safety of the payloads.